

'''simple docstring'''






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__   		=      logging.get_logger(__name__)


def 						_A	(       A__ ):






 """simple docstring"""



 __lowercase				    =					torch.load(A__	,							map_location='''cpu''' )
 if "model" in sd.keys():
  __lowercase				    =					torch.load(A__	,							map_location='''cpu''' )['''model''']

 # pop unnecessary weights
 __lowercase				    =					[
     '''decoder.version''',
     '''decoder.output_projection.weight''',
 ]
 for key in keys_to_delete:
  if key in sd:
   sd.pop(A__ )

 __lowercase				    =					{
     '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
     '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
     '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
     '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
 }
 for old_key, new_key in keys_to_rename.items():
  if old_key in sd:
   __lowercase				    =					sd.pop(A__ )

 __lowercase				    =					list(sd.keys() )
 for key in keys:
  if ".qkv_proj." in key:
   __lowercase				    =					sd[key]
   # We split QKV in separate Q,K,V

   __lowercase				    =					key.replace('''.qkv_proj.'''	,							'''.q_proj.''' )
   __lowercase				    =					key.replace('''.qkv_proj.'''	,							'''.k_proj.''' )
   __lowercase				    =					key.replace('''.qkv_proj.'''	,							'''.v_proj.''' )

   __lowercase				    =					value.shape[0]
   assert depth % 3 == 0
   # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
   # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
   __lowercase    ,      __lowercase    ,      __lowercase				    =					torch.split(A__	,							depth // 3	,							dim=0 )

   __lowercase				    =					q
   __lowercase				    =					k
   __lowercase				    =					v
   del sd[key]

 return sd


@torch.no_grad()
def 						_A	(       A__	,							A__	,							A__=None ):






 """simple docstring"""



 __lowercase				    =					load_checkpoint(A__ )

 if config is not None:
  __lowercase				    =					OPTConfig.from_pretrained(A__ )
 else:
  __lowercase				    =					OPTConfig()

 __lowercase				    =					OPTModel(A__ ).half().eval()
 model.load_state_dict(A__ )

 # Check results
 Path(A__ ).mkdir(exist_ok=A__ )
 model.save_pretrained(A__ )


if __name__ == "__main__":
 lowerCAmelCase__   		=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--fairseq_path''',
     type=str,
     help=(
         '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
         ''' https://huggingface.co/models?other=opt_metasq'''
     ),
 )
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
 lowerCAmelCase__   		=      parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)









"""simple docstring"""
import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property



@unittest.skipUnless(os.path.exists(a_)							,	"""Tatoeba directory does not exist.""")
class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    @cached_property
    def 				a__			(		self :	int ):





           '''simple docstring'''

           __magic_name__      				=    tempfile.mkdtemp()
           return TatoebaConverter(save_dir=UpperCamelCase_ )







    @slow
    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           self.resolver.convert_models(['heb-eng'] )







    @slow
    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           __magic_name__				,  __magic_name__      				=    self.resolver.write_model_card('opus-mt-he-en'				,    dry_run=UpperCamelCase_ )
           assert mmeta["long_pair"] == "heb-eng"